A Novel Wavelet Image Fusion Algorithm Based on Chaotic Neural Network

Authors:
Hong Zhang;Yan Cao;Yan-Feng Sun;Lei Liu
Affiliations:
College of Computer Science and Technology, Jilin University, Changchun, China 130012 and State Key Laboratory on Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin U ...;State Key Laboratory on Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, China 130012;State Key Laboratory on Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, China 130012;College of Computer Science and Technology, Jilin University, Changchun, China 130012
Venue:
FAW '08 Proceedings of the 2nd annual international workshop on Frontiers in Algorithmics
Year:
2008

Citing 7
Cited 0

Multisensor image fusion using the wavelet transform

Graphical Models and Image Processing
Chaotic simulated annealing by a neural network model with transient chaos

Neural Networks
Image fusion using steerable dyadic wavelet transform

ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
Wavelets and image fusion

ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
A Quickly Searching Algorithm for Optimization Problems Based on Hysteretic Transiently Chaotic Neural Network

ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Letter: A method to improve the transiently chaotic neural network

Neurocomputing
Nonlinear multiresolution signal decomposition schemes. I. Morphological pyramids

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, Transiently Chaotic Neural Network (TCNN) is used in wavelet image fusion method. This paper adopts the weighted average strategy for the fusion of the wavelet transform coefficients. The TCNN outputs the weighting coefficient of every wavelet transform pixel when the energy function of the neural network has achieved the global minimum. At the same time, the average gradient value of the region around every wavelet transform pixel gets the global maximum according to the relationship between the average gradient and energy. The wavelet transform coefficients of the fused image are got by using the weighting coefficients. The advantage of the algorithm is that the weighting coefficient is obtained through the dynamic searching optimization of the average gradient. Experiments show that the average gradient values of the fusion images using the proposed method are greater than the results using the region energy method. The TCNN method improves the performance of the fusion image effectively.